code: def train_ppo_module_with_bc_finetune(
      dataset: ray.data.Dataset,
      ppo_module_spec: SingleAgentRLModuleSpec,
  ) -> str:
      """Train an Actor with BC finetuning on dataset.
  
      Args:
          dataset: The dataset to train on.
          module_spec: The module spec of the PPORLModule that will be trained
              after its encoder and policy networks are pretrained with BC.
  
      Returns:
          The path to the checkpoint of the pretrained PPORLModule.
      """
      batch_size = 512
      learning_rate = 1e-3
      num_epochs = 10
  
      module = ppo_module_spec.build()
      # We want to pretrain the encoder and policy networks of the RLModule. We don't want
      # to pretrain the value network. The actor will use the Categorical distribution,
      # as its output distribution since we are training on the CartPole environment which
      # has a discrete action space.
      BCActorNetwork = BCActor(
          module.encoder, module.pi, torch.distributions.Categorical
      )
      optim = torch.optim.Adam(
          BCActorNetwork.parameters(), lr=learning_rate
      )
  
      for epoch in range(num_epochs):
          for batch in dataset.iter_torch_batches(
              batch_size=batch_size, dtypes=torch.float32
          ):
              action_dist = BCActorNetwork(batch)
              loss = -torch.mean(action_dist.log_prob(batch["actions"]))
              optim.zero_grad()
              loss.backward()
              optim.step()
          print(f"Epoch {epoch} loss: {loss.detach().item()}")
  
      checkpoint_dir = tempfile.mkdtemp()
      module.save_to_checkpoint(checkpoint_dir)
      return checkpoint_dir
  
file_path: ray/python/ray/rllib/examples/learner/train_w_bc_finetune_w_ppo.py
imports: import gymnasium
  import shutil
  import tempfile
  import torch
  from typing import Mapping
  import ray
  from ray import tune
  from ray.train import RunConfig, FailureConfig
  from ray.rllib.algorithms.ppo import PPOConfig
  from ray.rllib.algorithms.ppo.torch.ppo_torch_rl_module import PPOTorchRLModule
  from ray.rllib.algorithms.ppo.ppo_catalog import PPOCatalog
  from ray.rllib.core.models.base import ACTOR, ENCODER_OUT
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
label_desc: Use image node_id 1 for calling a global function with example usage: train_ppo_module_with_bc_finetune(dataset, ppo_module_spec) and returns: checkpoint_dir